Tid: 1 March 2010 kl 16.15-17.00.

Plats : Seminarierummet 3733, Institutionen för matematik, KTH, Lindstedts väg 25, plan 7. Karta!

Föredragshållare: Axel Sundén

Titel: Trading based on classification and regression trees (Examensarbete – Master thesis)

Abstract

This thesis investigates whether stock picking based on classification and regression trees can be implemented as a successful algorithmic trading system, if only based on technical analysis. To evaluate the performance of this method, a fictional portfolio was constructed from the Stockholm Stock Exchange OMX-30, traded on a five-year period.

By means of implementation, classification of the assets in the portfolio was initially conducted. By using threshold values of the weekly returns and comparison with the index of the portfolio, every asset was classified as either outperforming, neutral or underperforming. With a satisfactory classification, each asset that is considered as outperforming is held over a period of one week and at the end of the period the position is terminated and a rebalancing of the portfolio is made. If no assets are classified as outperforming, the portfolio is liquidated and invested at a risk-free rate, defined as the STIBOR 1 week rate.

When backtesting the model, we find that the hit ratio of the overall classification is slightly larger than 50 %. During backtesting over the complete trading period, it is found that an immense increase of portfolio value is generated. However, since the model is used in sample, no predictive validity outside the range can be made. For this reason, 10-fold cross-validation and resubstitution techniques are employed in order to increase the validity if used in an out-of-sample test. Further, a rolling Sharpe ratio is introduced to evaluate the risk-adjusted returns for both portfolios, and it is found that the rebalanced portfolio exhibits greater values.

It is concluded that algorithmic trading based on classification and regression trees can be effective in finding patterns that influence the stock prices and that it can form the foundation for an algorithmic trading system.

The full report (pdf)

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